Residual Momentum
Based on Blitz et al. (2011). Calculates momentum from residual returns after regressing out market factor (SPY). Captures 'pure' idiosyncratic momentum rather than beta-driven returns.
At a glance
What the backtest shows
- Beat buy-and-hold on 5 of 10 markets by annual return over 5 years.
- Had a smaller maximum drawdown than buy-and-hold on 10 of 10 markets.
- Best market by Sharpe ratio: USO (Sharpe 0.92, CAGR 24.0%).
- Weakest market: UNG (Sharpe -0.05, CAGR -10.3%).
Equity curve
Score and risk profile
Each bar is a percentile against every strategy on the same market and window (100 = best).
How it held up in sell-offs (GLD)
| Period | Strategy | Buy & hold |
|---|---|---|
| Q4 2018 sell-off | +2.5% | +6.6% |
| COVID crash (Feb–Mar 2020) | -3.6% | -3.6% |
| 2022 bear market | -5.1% | -7.3% |
| Market above its 200-day average (annualized) · 58% | +2.2% | +9.0% |
| Market below its 200-day average (annualized) · 42% | +3.8% | +5.4% |
Sell-off dates follow the US stock market. Above/below the 200-day average uses the previous day's position, so there is no look-ahead.
Out-of-sample check (GLD)
| Check | Value |
|---|---|
| Chosen parameters on their 3-year training windows (average Sharpe) | 0.63 |
| Same parameters in the following 6 months, when traded (Sharpe) | 0.35 |
| Default parameters over the same months (Sharpe) | 0.45 |
| Parameter re-selections / changes | 28 / 12 |
| Parameter settings with a positive Sharpe (full history) | 9 of 9 |
| Sharpe across settings: worst / median / best | 0.18 / 0.31 / 0.45 |
Out-of-sample periods start on 2013-01-04. Training-window figures are flattered by picking the best of 9 settings; the out-of-sample figure is what the strategy actually earned. A large drop, or a result that depends on one setting, points to overfitting. How walk-forward testing works.
Results by market (5 years)
| Market | CAGR | Buy & hold CAGR | Sharpe | Max drawdown | Buy & hold max DD | Volatility | Arena Score |
|---|---|---|---|---|---|---|---|
| USOUnited States Oil Fund | 24.0% | 20.2% | 0.92 | -27.5% | -36.2% | 26.6% | 86 |
| DBAInvesco DB Agriculture | 5.3% | 8.8% | 0.61 | -15.3% | -15.9% | 9.2% | 63 |
| SPYSPDR S&P 500 | 5.3% | 11.6% | 0.57 | -12.7% | -24.5% | 10.0% | 59 |
| GLDSPDR Gold Shares | 7.3% | 16.4% | 0.57 | -26.9% | -28.3% | 14.2% | 51 |
| SLViShares Silver Trust | 8.2% | 15.2% | 0.42 | -49.9% | -57.8% | 30.8% | 44 |
| DBCInvesco DB Commodity Index | 4.1% | 10.8% | 0.34 | -19.3% | -27.3% | 14.2% | 55 |
| BTC-USDBitcoin | 8.3% | 2.6% | 0.32 | -41.7% | -75.9% | 28.4% | 62 |
| ETH-USDEthereum | -11.5% | -15.1% | -0.04 | -58.1% | -79.0% | 36.9% | 30 |
| EURUSD=XEUR/USD | -0.4% | -0.7% | -0.05 | -12.3% | -17.2% | 5.2% | 52 |
| UNGUnited States Natural Gas | -10.3% | -30.7% | -0.05 | -72.5% | -93.3% | 42.1% | 51 |
Strategy metrics use daily returns without trading costs. Buy-and-hold figures are for the same market and window. Rows are sorted by Sharpe ratio.
GLD across time windows
Rules
Entry
Buy when residual momentum > 0 (positive alpha)
Exit
Exit when residual momentum <= 0
Indicators
Rolling beta to SPY Residual return = Actual - Beta * Market Cumulative residual momentum
Inputs
Beta_Window = 252 Momentum_Lookback = 252 Skip_Period = 21
Position sizing
Fixed 100% allocation
Risk controls
Skip most recent month to avoid reversal
Strategy evidence
- BTC-USD · Inspect evidence 2026-11-10
- DBA · Inspect evidence 2026-11-10
- DBC · Inspect evidence 2026-11-10
- ETH-USD · Inspect evidence 2026-11-10
- EURUSD=X · Inspect evidence 2026-11-10
- GLD · Inspect evidence 2026-11-10
- SLV · Inspect evidence 2026-11-10
- SPY · Inspect evidence 2026-11-10
- UNG · Inspect evidence 2026-11-10
- USO · Inspect evidence 2026-11-10
Only available, validated bundles appear below. Missing evidence is not a passing validation result.
Evidence coverage
10 / 10 declared strategy–market pairs validated
Notes
- Based on Blitz, Huij & Martens (2011) research
- Removes systematic risk, captures idiosyncratic momentum
- Uses simplified CAPM residual (SPY as market proxy)
Related strategies
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Backtests are historical simulations on daily data and do not include commissions or slippage. They do not guarantee future results and are not investment advice. See the methodology for how every strategy is tested.